Department of Media Sciences, College of Engineering, Anna University, Chennai, India.
JMIR Mhealth Uhealth. 2022 May 11;10(5):e15719. doi: 10.2196/15719.
The prevalence of obesity in India is increasing at an alarming rate. Obesity-related mHealth apps have proffered an exciting opportunity to remotely deliver obesity-related information. This opportunity raises the question of whether such apps are truly effective.
The aim of this study was to identify existing obesity-related mHealth apps in India and evaluate the potential of the apps' contents to promote health behavior change. This study also aimed to discover the general quality of obesity-related mHealth apps.
A systematic search for obesity-related mHealth apps was conducted in both the Google Play Store and the Apple App Store. The features and quality of the sample apps were assessed using the Mobile Application Rating Scale (MARS) and the potential of the sample apps' contents to promote health behavior change was assessed using the PRECEDE-PROCEED Model (PPM).
A total of 13 apps (11 from the Google Play Store and 2 from the Apple App Store) were considered eligible for the study. The general quality of the 13 apps assessed using MARS resulted in mean scores ranging from 1.8 to 3.7. The bivariate Pearson correlation between the MARS rating and app user rating failed to establish statistically significant results. The multivariate regression analysis result indicated that the PPM factors are significant determinants of health behavior change (F=63.186; P<.001) and 95.5% of the variance (R=0.955; P<.001) in the dependent variable (health behavior change) can be explained by the independent variables (PPM factors).
In general, mHealth apps are found to be more effective when they are based on theory. The presence of PPM factors in an mHealth app can greatly influence the likelihood of health behavior change among users. So, we suggest mHealth app developers consider this to develop efficient apps. Also, mHealth app developers should consider providing health information from credible sources and indicating the sources of the information, which will increase the perceived credibility of the apps among the users. We strongly recommend health professionals and health organizations be involved in the development of mHealth apps. Future research should include mHealth app users to understand better the apps' effectiveness in bringing about health behavior change.
印度的肥胖患病率正以惊人的速度增长。与肥胖相关的移动健康应用程序提供了一个远程提供肥胖相关信息的令人兴奋的机会。这一机会提出了这样一个问题,即此类应用程序是否真的有效。
本研究旨在确定印度现有的与肥胖相关的移动健康应用程序,并评估应用程序内容促进健康行为改变的潜力。本研究还旨在发现与肥胖相关的移动健康应用程序的一般质量。
在谷歌 Play 商店和苹果应用商店中对与肥胖相关的移动健康应用程序进行了系统搜索。使用移动应用程序评级量表 (MARS) 评估了样本应用程序的功能和质量,并使用 PRECEDE-PROCEED 模型 (PPM) 评估了样本应用程序内容促进健康行为改变的潜力。
共有 13 个应用程序(11 个来自谷歌 Play 商店,2 个来自苹果应用商店)被认为符合研究条件。使用 MARS 对 13 个应用程序的总体质量进行评估,得出的平均得分为 1.8 至 3.7。MARS 评分与应用程序用户评分之间的双变量 Pearson 相关未能建立统计学上显著的结果。多元回归分析结果表明,PPM 因素是健康行为改变的重要决定因素(F=63.186;P<.001),并且独立变量(PPM 因素)可以解释因变量(健康行为改变)95.5%的方差(R=0.955;P<.001)。
总的来说,基于理论的移动健康应用程序更有效。移动健康应用程序中存在 PPM 因素可以极大地影响用户健康行为改变的可能性。因此,我们建议移动健康应用程序开发者考虑这一点,以开发高效的应用程序。此外,移动健康应用程序开发者应考虑从可信来源提供健康信息,并指出信息来源,这将提高应用程序在用户中的可信度。我们强烈建议健康专业人员和健康组织参与移动健康应用程序的开发。未来的研究应该包括移动健康应用程序的用户,以更好地了解应用程序在带来健康行为改变方面的有效性。